Machine-Learning Algorithms Can Predict Suicide Risk More Readily than Clinicians
by Matthew Hutson
Each year in the United States, more than 40,000 people die by suicide, and from 1999 to 2014, the suicide rate increased 24 percent. You might think that after generations of theories and data, we would be close to understanding how to prevent self-harm, or at least predict it. But a new study concludes that the science of suicide prediction is dismal, and the established warning signs about as accurate as tea leaves.
There is, however, some hope. New research shows that machine-learning algorithms can dramatically improve our predictive abilities on suicides. In a new survey in the February issue of Psychological Bulletin, researchers looked at 365 studies from the past 50 years that included 3,428 different measurements of risk factors, such as genes, mental illness and abuse.
Colin Walsh, an internist and data scientist at Vanderbilt University Medical Center, along with FSU’s Franklin and Ribeiro, looked at millions of anonymized health records and compared 3,250 clear cases of nonfatal suicide attempts with a random group of patients. To make their prediction method widely scalable, they restricted themselves to factors that would be documented in routine clinical encounters, such as demographics, medications, prior diagnoses and body mass index. Then they let a computer churn through the data and find patterns that would predict suicide attempts within various time frames, from a week to two years.
The accuracy score for each algorithm could range from 0.5 to 1, with 0.5 being no better than chance and 1 being perfect prediction. For comparison, the single factors from the meta-analysis achieved scores of about 0.58, little better than flipping a coin. The computer, however, achieved scores ranging from 0.86, when predicting whether someone would attempt suicide within two years, to 0.92, when looking ahead one week.
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